blokus-engine 0.3.5

No project description provided

Blokus Engine

Checkout the hosted GUI here

Project Outline

This repository contains several main sections. Most of the code is train a neural network using self-play and Monte Carlo Tree Search. During the process you have a neural network that acts as the brains of your players, and you have a game which is progressively getting played out. To simulate a game, all four players use the same network to determine which move they should take. They use MCTS to explore a tree of possible moves, and the game ultimately takes one path down this tree. Once the game is finished, the model can use all of this data it generated to train and improve itself. Then another game can be simulated using the better model to create more data. This is the basic cycle that allows the neural network to improve. To speed things up, you can have one neural network, and you can have multiple games going in parralel at the same time. This means you can batch requests from multiple games and you can generate more data at the same time. This project uses a client-server architecture where the neural network is hosted on a server, and the clients each query the server as they simulate a game. The server code uses Python and Pytorch, and that is all located in the model_server directory. Meanwhile each client uses Rust code in the self_play directory. This code is built on top of the blokus directory which contains all of the core game logic. Lastly, there is a GUI (that currently does not work yet) to play a game against the trained model.

Training Configuration

All of the configuration for training is done in the model/training.py config class. All of the values in this config are listed below, along with an example config file. The total number of games played during training is the number of clients times the number of games per client times the number of training rounds. For example if you have 10 clients, each generating 2 games worth of data per training round, and you train for 1 round, you will have 20 games worth of data. AlphaZero trained on 21 million games of Go.

Variable	Description	AlphaZero Value
TRAINING_ROUNDS	The number of training rounds to run	4,200
BUFFER_CAPACITY	The number of data points to store in the replay buffer	1,000,000 games
LEARNING_RATE	The learning rate of the neural network	.01 -> .0001 with scheduler
BATCH_SIZE	The number of data points per batch	2,048
TRAINING_STEPS	The number of training steps to run each round	700,000
NN_WIDTH	The number of filters in each convolutional layer	256
NN_DEPTH	The number of residual blocks in the neural network	20
NUM_CLIENTS	The number of clients to run	5,000
GAMES_PER_CLIENT	The number of games each client generates per round	1
SIMS_PER_MOVE	The number of simulations to run during MCTS to derive a policy	800
SAMPLE_MOVES	The number of moves in a game that sample from the MCTS policy instead of picking the max to encourage exploration	30
C_BASE	Constant for UCB formula to balance exploration and exploitation	19,652
C_INIT	Constant for UCB formula to balance exploration and exploitation	1.25
DIRICHLET_ALPHA	The alpha parameter of the Dirichlet distribution which adds noise to the root node during MCTS to promote exploration	0.03
EXPLORATION_FRAC	Fraction used to mix noise and prior probability	0.25

Usage:

GUI

To open the GUI in the browser run the model server and the proxy server then run: cd gui trunk serve --open

Training

To train locally, run:

python model/training.py --test --workers 1 --dim 20 --load <model_path> --save <new_path>

To run a job on the HPC using 32 CPU cores, 240GB of memory, and an RTX2080 card, you can do:

sbatch -p mixed -N 1 -n 32 --mem=240G --gres=gpu:rtx2080:1 train.sh

Testing

To test the model, you can run the following command:

python model/testing.py [num_games] [model_path] [benchmark_model_path]

References

• https://sebastianbodenstein.com/post/alphazero/
• https://arxiv.org/pdf/1712.01815.pdf
• https://arc.net/folder/7FE3479D-1752-401F-9DC3-49AAD02B5DF3